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Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score
BACKGROUND: To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. METHODS: Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with Ig...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954570/ https://www.ncbi.nlm.nih.gov/pubmed/31924265 http://dx.doi.org/10.1186/s13075-019-2090-9 |
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author | Li, Jieqiong Peng, Yu Zhang, Yuelun Zhang, Panpan Liu, Zheng Lu, Hui Peng, Linyi Zhu, Liang Xue, Huadan Zhao, Yan Zeng, Xiaofeng Fei, Yunyun Zhang, Wen |
author_facet | Li, Jieqiong Peng, Yu Zhang, Yuelun Zhang, Panpan Liu, Zheng Lu, Hui Peng, Linyi Zhu, Liang Xue, Huadan Zhao, Yan Zeng, Xiaofeng Fei, Yunyun Zhang, Wen |
author_sort | Li, Jieqiong |
collection | PubMed |
description | BACKGROUND: To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. METHODS: Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the “IgG4-RD CS” prediction model for the comprehensive assessment of IgG4-RD. To evaluate the value of the IgG4-RD CS in the assessment of disease severity, patients in different IgG4-RD CS groups and in different IgG4-RD responder index (RI) groups were compared. RESULTS: PCA indicated that the 22 baseline variables of IgG4-RD patients mainly consisted of inflammation, high serum IgG4, multi-organ involvement, and allergy-related phenotypes. Cluster analysis classified patients into three groups: cluster 1, inflammation and immunoglobulin-dominant group; cluster 2, internal organs-dominant group; and cluster 3, inflammation and immunoglobulin-low with superficial organs-dominant group. Moreover, there were significant differences in serum and clinical characteristics among subgroups based on the CS and RI scores. IgG4-RD CS had a similar ability to assess disease severity as RI. The “IgG4-RD CS” prediction model was established using four independent variables including lymphocyte count, eosinophil count, IgG levels, and the total number of involved organs. CONCLUSION: Our study indicated that newly diagnosed IgG4-RD patients could be divided into three subgroups. We also showed that the IgG4-RD CS had the potential to be complementary to the RI score, which can help assess disease severity. |
format | Online Article Text |
id | pubmed-6954570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69545702020-01-14 Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score Li, Jieqiong Peng, Yu Zhang, Yuelun Zhang, Panpan Liu, Zheng Lu, Hui Peng, Linyi Zhu, Liang Xue, Huadan Zhao, Yan Zeng, Xiaofeng Fei, Yunyun Zhang, Wen Arthritis Res Ther Research Article BACKGROUND: To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. METHODS: Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the “IgG4-RD CS” prediction model for the comprehensive assessment of IgG4-RD. To evaluate the value of the IgG4-RD CS in the assessment of disease severity, patients in different IgG4-RD CS groups and in different IgG4-RD responder index (RI) groups were compared. RESULTS: PCA indicated that the 22 baseline variables of IgG4-RD patients mainly consisted of inflammation, high serum IgG4, multi-organ involvement, and allergy-related phenotypes. Cluster analysis classified patients into three groups: cluster 1, inflammation and immunoglobulin-dominant group; cluster 2, internal organs-dominant group; and cluster 3, inflammation and immunoglobulin-low with superficial organs-dominant group. Moreover, there were significant differences in serum and clinical characteristics among subgroups based on the CS and RI scores. IgG4-RD CS had a similar ability to assess disease severity as RI. The “IgG4-RD CS” prediction model was established using four independent variables including lymphocyte count, eosinophil count, IgG levels, and the total number of involved organs. CONCLUSION: Our study indicated that newly diagnosed IgG4-RD patients could be divided into three subgroups. We also showed that the IgG4-RD CS had the potential to be complementary to the RI score, which can help assess disease severity. BioMed Central 2020-01-10 2020 /pmc/articles/PMC6954570/ /pubmed/31924265 http://dx.doi.org/10.1186/s13075-019-2090-9 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Li, Jieqiong Peng, Yu Zhang, Yuelun Zhang, Panpan Liu, Zheng Lu, Hui Peng, Linyi Zhu, Liang Xue, Huadan Zhao, Yan Zeng, Xiaofeng Fei, Yunyun Zhang, Wen Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title | Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title_full | Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title_fullStr | Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title_full_unstemmed | Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title_short | Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score |
title_sort | identifying clinical subgroups in igg4-related disease patients using cluster analysis and igg4-rd composite score |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954570/ https://www.ncbi.nlm.nih.gov/pubmed/31924265 http://dx.doi.org/10.1186/s13075-019-2090-9 |
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